Adaptive learning of actionable statements in natural language conversation

ABSTRACT

Identifying actionable statements in communications may include: extracting features from at least one training statement; training a pattern recognition module to identify one or more types of patterns in actionable statements based at least in part on the features; and generating an actionable statement identification model using the trained action verb module and the trained pattern recognition module. Identifying actionable statements in communications is preferably adaptive in a continuous manner (e.g. based on user feedback), and may also include: determining whether a statement includes an actionable statement; predicting an actionable statement class of the actionable statement based on a pattern represented in the statement; and outputting the predicted actionable statement class to a user. Corresponding systems and computer program products are also disclosed.

BACKGROUND

The present invention relates to machine learning, and morespecifically, this invention relates to adaptive learning of actionablestatement patterns in linguistic communications.

The advent of electronic communications has been a great boon forproductivity, creativity, collaboration, and a host of otherwell-established benefits across almost every sector of the worldeconomy. Typical electronic communications may involve initiating andexchanging information about work activities in conversations withothers over channels such as email, chat, and messaging. A major part ofemployees' conversations entail statements on what work (actions) getsdone, and by whom.

However, the increased frequency and speed at which communications areexchanged has generally resulted in a drastic increase in the volume ofcommunication exchanged. This, in turn, generally results in individualcommunicators, especially humans, receiving a volume of communicationswhich is impossible or impractical to process effectively andefficiently within the time constraints imposed by traditional workschedules. As a result, important action items represented in thecommunications backlog are often missed, detrimentally impacting theparties with an interest in the action items being efficiently andeffectively completed.

To address this issue, various communication tools such as emailprograms, electronic calendars, etc. include features which allow a userto “flag” certain communications for future follow-up action, and toindicate priority of certain communications, in order to assist the userorganize the various action items represented/contained in the user'scommunication backlog. However, these manual organization techniques andtools still rely on the user to process the information contentrepresented in the communications, decide the appropriate action, andtake that action (e.g. set a reminder, priority level, etc.). Therefore,these manual organization techniques assist the user's organization ofthe voluminous communications, but detrimentally do so by adding moretasks for the user to perform to achieve the organization, and provideno assistance with respect to communications the user fails to processand manually organize.

Automating the process of parsing and organizing communications, e.g.using natural language processing (NLP) techniques that leverage machinelearning principles, also prove inadequate tools to solve the problem.While the automated nature of these techniques relieves the user fromhaving to manually process and organize the voluminous body of incomingcommunications, these techniques suffer from limitations inherent tomachine learning. For instance, the accuracy of the automated processingalgorithm is a function of the propriety of the training set used toteach the machine how to parse the communications. Where so much of theinformational content and meaning of modern communications isrepresented in the context of the communication—both the context of thetext making up the communication and the context in which thecommunication is exchanged—the statistical approaches employed in modernmachine learning are incapable of adapting to the wide range of contextsnecessary for effective and efficient automation of processing moderncommunications.

For instance, actionable statements are often expressed in a variety offorms (e.g. patterns) and/or language structures, and may followdifferent styles due to personal preference, geographic location,culture, context, and complexity of the language in which thecommunication is being exchanged. From a language point of view, not allcommunications come in complete and well-formed sentences, but sometimesinclude grammatical and/or syntax errors, or include incompletesentences. In addition, sometimes actions may be implied rather thanexplicitly stated. Further, many scenarios may require parsing severalstatements or interactions to identify an actionable statement.

Moreover, finding and training, or manually encoding, comprehensive andgeneric patterns and rules that cover all possible actions, contexts,etc., yet precisely and specifically capture actionable statements is anexceedingly difficult, if not impossible task. In addition, theseconventional approaches are not scalable as new forms are encountered innew communications, contexts (e.g. different sectors of the economyoften use different jargon and attach different meanings to the sameterms), new communicators become involved in the exchange ofcommunications, etc.

As a result, communications are often improperly handled by automatedtechniques, resulting in sometimes humorous misunderstandings, asexemplified by personal digital assistants, predictive dictionaries,etc. However, these limitations result in real and significant economiclosses as communications are improperly handled, and thus action itemsare missed or mishandled.

Accordingly, it would be of great utility to provide systems,techniques, and computational tools capable of automaticallyinterpreting communications using appropriate context to identifyactionable statements, while overcoming the limitations of theconventional approaches set forth above.

SUMMARY

In one embodiment, a computer-implemented method is configured foridentifying actionable statements in communications. The methodincludes: extracting features from at least one training statement;training a pattern recognition module to identify one or more types ofpatterns in actionable statements based at least in part on thefeatures; and generating an actionable statement identification modelusing the trained action verb module and the trained pattern recognitionmodule.

In another embodiment, a computer-implemented method is configured foridentifying actionable statements in communications. The methodincludes: determining whether a statement includes an actionablestatement; in response to determining the statement includes anactionable statement, predicting an actionable statement class of theactionable statement based on a pattern represented in the statement;and outputting the predicted actionable statement class to a user. Thepredicted actionable statement class is selected from among a pluralityof actionable statement classes including promise(s) and request(s).

In yet another embodiment, a computer program product is configured foridentifying actionable statements in communications. The computerprogram product includes a computer readable storage medium havingprogram instructions embodied therewith, and the program instructionsare executable by a processor to cause the processor to: determine,using the processor, whether the statement includes an actionablestatement; in response to determining a statement includes an actionablestatement, predict, using the processor, an actionable statement classof the actionable statement based on a pattern represented in thestatement; and output, using the processor, the predicted actionablestatement class to a user. The predicted actionable statement class isselected from among a plurality of actionable statement classesincluding question(s), promise(s) and request(s).

In still yet another embodiment, a system includes a processor and logicintegrated with and/or executable by the processor. The logic isconfigured to: determine, using the processor, whether a statementincludes an actionable statement; in response to determining thestatement includes an actionable statement, predict, using theprocessor, an actionable statement class of the actionable statementbased on a pattern represented in the statement; and output, using theprocessor, the predicted actionable statement class to a user. Thepredicted actionable statement class is selected from among a pluralityof actionable statement classes including question(s), promise(s) andrequest(s).

Other aspects and embodiments of the present invention will becomeapparent from the following detailed description, which, when taken inconjunction with the drawings, illustrate by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a network architecture, in accordance with oneembodiment.

FIG. 2 shows a representative hardware environment that may beassociated with the servers and/or clients of FIG. 1, in accordance withone embodiment.

FIGS. 3A-3B depict exemplary statements including one or more of actioninstances and action verbs, according to several embodiments.

FIG. 3C depicts a sentence lacking an actionable statement, according toone approach.

FIGS. 4A-4B depict exemplary statements and features thereof, accordingto several embodiments.

FIG. 5 depicts an exemplary organization for various models ofactionable statement adaptive learning, according to one embodiment.

FIG. 6 is a simplified schematic of an adaptive model for learning andidentifying action statements in communications, according to oneembodiment.

FIG. 7 is a flowchart of a method, according to one embodiment.

FIG. 8 is a flowchart of a method, according to one embodiment.

DETAILED DESCRIPTION

The following description is made for the purpose of illustrating thegeneral principles of the present invention and is not meant to limitthe inventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.

Definitions

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

It must also be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless otherwise specified. It will be further understood thatthe terms “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The presently disclosed inventive concepts provide intelligentassistance in the work environment, and in particular address the issueof actionable statement identification in human conversations. Inpreferred approaches, the actionable statement identification isperformed using one or more continuously adaptive models which may beconfigured, e.g. through adaptation, to uniquely identify actionablestatements based at least in part on textual information derived fromcommunications between an individual or a particular group ofindividuals.

As referred to herein, statements expressed in natural language andexchanging information about work activities in conversations withothers over channels such as email, chat, and messaging are considered“actionable statements” when the conversations entail statements on whatwork (actions) gets done, and by whom.

As understood herein, an actionable statement includes a combination ofa verb and an action pattern.

As understood herein, an action pattern is defined as a collection of aspecific ordering of tokens and part of speech (POS) tags, and otherfeatures derived from a statement, some of which are related to theverb. In various embodiments, action patterns may include any featuresthat may be identified using a feature extraction capability of anatural language processing engine, as would be understood by a personhaving ordinary skill in the art upon reading the present descriptions.

According to preferred embodiments, for an action pattern AP, let V be aset of all verbs, T be a list of all tokens except V, and G be a list ofPOS tags of T represented in a statement S, which may include one ormore sentences. The action pattern AP in statement S may be defined asAP=(v; d; f) where v is an element of the set of verbs V; d is anordered sequence of extracted and constructed features f; and f is anelement of the union of sets T and G.

As understood herein, actionable statement identification may beconsidered a process involving the following defining features. Given astatement S, a set of predefined action classes C, and an action patternAP in S, actionable statement identification includes correctlyselecting class c (which is an element of the set C) for AP. Of course,actionable statement identification may include identifying pluralaction patterns in a sentence, in plural sentences, pluralcommunications, etc. according to various embodiments of the presentlydisclosed inventive concepts.

As understood herein, when an action pattern specifies an actioninstance (also referred to as positive action pattern), the associatedverb is defined herein as an “action verb.”

As understood herein, an action instance is a portion of a statement,focused around a verb and one or more contextual elements of thestatement, preferably at least one contextual element positioned before(e.g. to the left, in English) and at least one contextual elementpositioned after (e.g. to the right, in English) of the focus verb. Insome approaches, an actionable statement may include a contextualelement positioned after the focus verb, but not before the focus verb.In still more approaches, an actionable statement may include acontextual element positioned before the focus verb, but not after thefocus verb.

According to particularly preferred embodiments, at least two classes ofactionable statement, or “action classes” are included in the set ofactionable statements subject to identification: promises and requests.A request is a communication including an action that the sender of thecommunication seeks to be performed by another party (e.g., a messagerecipient), for instance, “Can you please send me the file?” or “Send methe file by noon”, while a promise generally considered a communicationincluding a statement in which a commitment is made by the sender of themessage, typically to one or more recipients of the communication orthird party (e.g., “I will draft and send you the financial statementnext Monday.” or “I will draft and send Bob the financial statement nextMonday.”).

Of course, the foregoing are merely exemplary action patterns, and thepresently disclosed inventive concepts may extend to additional and/oralternative types of action pattern, such as questions (which may or maynot include requests), instructions (which may include, for example,direct orders rather than requests), representations (which may include,for example, indicia that a task has been completed), refusals (e.g. anegative response to a request), withdrawals (e.g. a rescission of apreviously presented promise), etc. as would be understood by a personhaving ordinary skill in the art upon reading the present descriptions.

More specifically, and according to several embodiments, given twoparties of people X and Y and an action A where |X|>0 and |Y|>0, arequest and a promise may be defined as follows.

In preferred embodiments, a Request R is a tuple R=(X; Y; A; T) where Xis requesting (assigning) Y to perform Action A, optionally by a duedate T.

In addition, according to preferred approaches a Promise P is a tupleP=(X; Y; A; T) where X is committing to party Y that X will performAction A, optionally by a due date T.

The foregoing definitions are provided to facilitate clarity ofunderstanding with respect to the presently disclosed inventiveconcepts, and may include essential features for the operation ofvarious embodiments of those concepts, as would be understood by aperson having ordinary skill in the art upon reading the presentdescriptions. However, the foregoing definitions should not beconsidered exclusive of other equivalent embodiments that would beunderstood by a person having ordinary skill in the art upon reading thepresent disclosure.

General Embodiments

In various implementations, the presently disclosed inventive conceptsmay include one or more of the following general embodiments ofactionable statement identification in communications.

In one general embodiment, a computer-implemented method is configuredfor identifying actionable statements in communications. The methodincludes: extracting features from at least one training statement;training a pattern recognition module to identify one or more types ofpatterns in actionable statements based at least in part on thefeatures; and generating an actionable statement identification modelusing the trained action verb module and the trained pattern recognitionmodule.

In another general embodiment, a computer-implemented method isconfigured for identifying actionable statements in communications. Themethod includes: determining whether a statement includes an actionablestatement; in response to determining the statement includes anactionable statement, predicting an actionable statement class of theactionable statement based on a pattern represented in the statement;and outputting the predicted actionable statement class to a user. Thepredicted actionable statement class is selected from among a pluralityof actionable statement classes including promise(s) and request(s).

In yet another general embodiment, a computer program product isconfigured for identifying actionable statements in communications. Thecomputer program product includes a computer readable storage mediumhaving program instructions embodied therewith, and the programinstructions are executable by a processor to cause the processor to:determine, using the processor, whether the statement includes anactionable statement; in response to determining a statement includes anactionable statement, predict, using the processor, an actionablestatement class of the actionable statement based on a patternrepresented in the statement; and output, using the processor, thepredicted actionable statement class to a user. The predicted actionablestatement class is selected from among a plurality of actionablestatement classes including promise(s) and request(s).

In still yet another general embodiment, a system includes a processorand logic integrated with and/or executable by the processor. The logicis configured to: determine, using the processor, whether a statementincludes an actionable statement; in response to determining thestatement includes an actionable statement, predict, using theprocessor, an actionable statement class of the actionable statementbased on a pattern represented in the statement; and output, using theprocessor, the predicted actionable statement class to a user. Thepredicted actionable statement class is selected from among a pluralityof actionable statement classes including promise(s) and request(s).

General Computing Concepts

FIG. 1 illustrates an architecture 100, in accordance with oneembodiment. As shown in FIG. 1, a plurality of remote networks 102 areprovided including a first remote network 104 and a second remotenetwork 106. A gateway 101 may be coupled between the remote networks102 and a proximate network 108. In the context of the presentarchitecture 100, the networks 104, 106 may each take any formincluding, but not limited to a LAN, a WAN such as the Internet, publicswitched telephone network (PSTN), internal telephone network, etc.

In use, the gateway 101 serves as an entrance point from the remotenetworks 102 to the proximate network 108. As such, the gateway 101 mayfunction as a router, which is capable of directing a given packet ofdata that arrives at the gateway 101, and a switch, which furnishes theactual path in and out of the gateway 101 for a given packet.

Further included is at least one data server 114 coupled to theproximate network 108, and which is accessible from the remote networks102 via the gateway 101. It should be noted that the data server(s) 114may include any type of computing device/groupware. Coupled to each dataserver 114 is a plurality of user devices 116. User devices 116 may alsobe connected directly through one of the networks 104, 106, 108. Suchuser devices 116 may include a desktop computer, lap-top computer,hand-held computer, printer or any other type of logic. It should benoted that a user device 111 may also be directly coupled to any of thenetworks, in one embodiment.

A peripheral 120 or series of peripherals 120, e.g., facsimile machines,printers, networked and/or local storage units or systems, etc., may becoupled to one or more of the networks 104, 106, 108. It should be notedthat databases and/or additional components may be utilized with, orintegrated into, any type of network element coupled to the networks104, 106, 108. In the context of the present description, a networkelement may refer to any component of a network.

According to some approaches, methods and systems described herein maybe implemented with and/or on virtual systems and/or systems whichemulate one or more other systems, such as a UNIX system which emulatesan IBM z/OS environment, a UNIX system which virtually hosts a MICROSOFTWINDOWS environment, a MICROSOFT WINDOWS system which emulates an IBMz/OS environment, etc. This virtualization and/or emulation may beenhanced through the use of VMWARE software, in some embodiments.

In more approaches, one or more networks 104, 106, 108, may represent acluster of systems commonly referred to as a “cloud.” In cloudcomputing, shared resources, such as processing power, peripherals,software, data, servers, etc., are provided to any system in the cloudin an on-demand relationship, thereby allowing access and distributionof services across many computing systems. Cloud computing typicallyinvolves an Internet connection between the systems operating in thecloud, but other techniques of connecting the systems may also be used.

FIG. 2 shows a representative hardware environment associated with auser device 116 and/or server 114 of FIG. 1, in accordance with oneembodiment. Such figure illustrates a typical hardware configuration ofa workstation having a central processing unit 210, such as amicroprocessor, and a number of other units interconnected via a systembus 212.

The workstation shown in FIG. 2 includes a Random Access Memory (RAM)214, Read Only Memory (ROM) 216, an I/O adapter 218 for connectingperipheral devices such as disk storage units 220 to the bus 212, a userinterface adapter 222 for connecting a keyboard 224, a mouse 226, aspeaker 228, a microphone 232, and/or other user interface devices suchas a touch screen and a digital camera (not shown) to the bus 212,communication adapter 234 for connecting the workstation to acommunication network 235 (e.g., a data processing network) and adisplay adapter 236 for connecting the bus 212 to a display device 238.

The workstation may have resident thereon an operating system such asthe Microsoft Windows® Operating System (OS), a MAC OS, a UNIX OS, etc.It will be appreciated that a preferred embodiment may also beimplemented on platforms and operating systems other than thosementioned. A preferred embodiment may be written using XML, C, and/orC++ language, or other programming languages, along with an objectoriented programming methodology. Object oriented programming (OOP),which has become increasingly used to develop complex applications, maybe used.

Of course, this logic may be implemented as a method on any deviceand/or system or as a computer program product, according to variousembodiments.

Actionable Statement Identification

Returning now to the inventive concepts of systems, techniques, etc. forlearning to identify actionable statements in communications, andimplement those lessons to adaptively identify actionable statements ina personalized, domain-specific, continuous, and/or scalable mannerinvolve various features, functions, components, etc. which may beutilized individually or in concert, in various embodiments. Preferredembodiments will employ a combination of these capabilities, andparticularly preferred embodiments include all of the aforementionedcapabilities.

Illustrative implementations of these features will be described infurther detail below with reference to the Figures, which a skilledartisan will appreciate to be merely exemplary embodiments, and shouldnot be considered limiting on the scope of the present disclosures.Descriptions referring to a particular embodiment should not beconstrued as exclusive of other embodiments unless expressly stated, butalso should not be construed as requiring features described with regardto other embodiments unless expressly described as being a necessity ofthose embodiments.

The actionable statement identification systems, techniques, etc.described herein preferably are characterized by being adaptive tochanges in communication style, behavior, etc. for individuals involvedand/or referenced in the communications, in one embodiment.

The adaptive nature of preferred implementations of the presentlydisclosed inventive concepts confers advantageous ability to learn andimplement new and/or modified approaches to identifying actionablestatements based on changing text content, behavior patterns,communication context, etc. as would be understood by a person havingordinary skill in the art upon reading these descriptions. Accordingly,these concepts represent an improvement over traditional approaches toparsing human communications using machine learning techniques, as theadaptive embodiments disclosed herein are not limited by the trainingset utilized to teach the machine to parse the communications, butrather may continue to adjust to new information and behaviors ascommunications are carried out over the course of time.

In another embodiment the actionable statement identification systems,techniques, etc. preferably are characterized by being adaptive in acontinuous manner, such that actionable statement identification isconstantly “evolving” and adjusting to changes in communication style,content, behavior, context, etc.

In more embodiments, the presently disclosed actionable statementidentification systems, techniques, etc. preferably are characterized bybeing personalized, such that actionable statement identification may beconfigured according to an individual's particular communication style,content, behavior, context, etc. In addition, these systems, techniques,etc. may be domain-specific, such that actionable statementidentification is configured according to the communication style,content, behavior, context, etc. of a particular group of individuals.As described in further detail below with reference to FIG. 6,personalized and/or domain-specific actionable statement identificationmay be accomplished using a plurality of models, preferably operating incooperation.

In still more embodiments, the presently disclosed inventive conceptsare characterized by scalability, such that the capability todynamically learn to identify actionable statements, and implement thoselessons, enables the systems and techniques described herein to rapidlyadopt to new communicators and/or new contexts introduced to an existingcommunication network, and/or adjust to the departure of communicatorsor removal of existing contexts from a communication network. Thiscapability facilitates identification of actionable patterns in a muchmore efficient and effective manner than conventional approaches basedon machine learning (particularly those relying on predefined trainingsets), ontology-based approaches relying on manually or statisticallydefined rules to parse communication content, etc. as would beunderstood by a person having ordinary skill in the art upon reading thepresent descriptions.

Of course, the instant descriptions also include embodimentscharacterized by one or more of the aforementioned adaptive, continuous,personalized, domain-specific, and/or scalable features andfunctionalities, without departing from the scope of the inventiveconcepts disclosed herein.

Particularly in the context of electronic communications, where acommunication includes a promise, the sender of the communication isusually the entity committing to a promise included in thecommunication, and the recipient of the communication is most often thebeneficiary of the promise. Similarly, where a communication includes arequest, the sender of the communication is most often the entity makingthe request, and the recipient of the communication is most often theentity from whom the requested action is sought. Of course, somepromises and requests may be included in communications where thebeneficiary is a third party, who may or may not be included in thecommunication.

The presently disclosed inventive concepts are configured to adaptivelylearn and identify actionable statements within promises and requests,as well as other forms of statements typically included in naturallanguage communications. The processes leverage concepts established inknown natural language processing and feature identification techniques,such as identification of tokens (actual terms) used in statements, andtagging of features according to part of speech (POS) and other commonlabels, e.g. person type (NER), action verb (ACT), enclosed action verb(ACT_E), verb mood (MOOD), etc. as would be understood by a personhaving ordinary skill in the art upon reading the present descriptions.

Features and Feature Extraction

It is advantageous, in preferred embodiments, to extract features withrespect to each verb in a sentence. The features may be used to learnvarious action patterns that identify action instances from the trainingsamples. Features, in various embodiments, include one or more of tagand language token types. Preferably, tags and language token types mayinclude/designate one or more of: (1) a Pronoun (PRON) token, anauxiliary (AUX) token, and/or one or more personal named entities (NER)tag that are related to the focus verb; (2) a mood of the verb; (3)whether the verb is an Action verb; (4) whether the verb enclosesanother verb/s; (5) whether a number of enclosed action verbs is greaterthan zero; and (6) a tense of the verb.

For text parsing and extracting exemplary features as set forth above,in some approaches the presently disclosed inventive concepts mayleverage one or more known natural language parsing engines. Inpreferred approaches, and according to several exemplary embodimentpresented herein, an IBM BIGINSIGHTS SYSTEM T™ which has an associatedquery language, called AQL (Annotation Query Language) may be used toget basic text annotations such as POS tags. However, any suitable NLPengine that would be appreciated by a person having ordinary skill inthe art upon reading these disclosures may be employed, in otherembodiments. Preferably, the NLP engine locates tokens and tags that areassociated with the target verb, and may use the result of a NLPdependency parser, in some approaches.

For the token types “pronoun,” and “auxiliary,” and the tag of NER, itis advantageous to look in the left context (e.g. phrase 304 as shown inFIGS. 3A-3C) of the verb (e.g. verb 302 as shown in FIGS. 3A-3C) inthese results. If there are more than one of “pronoun,” and “auxiliary,”and the tag of NER available in the left context, the closest one to theverb is analyzed to evaluate the presence of an actionable statement.

The mood of a verb changes based on the place it appears in the sentenceand how it is related to prepositions (exemplary moods are: normal,imperative, infinitive, etc. as would be understood by a person havingordinary skill in the art upon reading the present descriptions), andalso may be leveraged to evaluate the presence of an actionablestatement.

The notion of enclosed verb in the list of features above refers to averb that is not the dominating verb for the action instance but states“what/how” actions for the action instance. For example, in FIG. 4A, theverb “discuss” is enclosed by the verb “schedule” and thereforeconsidered an enclosed verb. When a verb is enclosing one or more verbsand at least one of them is an action verb, then it has >0 enclosedaction verbs. In such sentences, if the main verb is not an action verb(such as in the case of “Please let me know the results,” in which “let”is not an action verb), it is of interest to find whether the enclosedverb is an action verb (such as “know” in this case), because if theenclosed verb is an action verb, this factor contributes to identifyingthe statement as an actionable statement.

Finally, the tense of the verb may be used as another feature, as itcontributes to the identification of actionable statements because someverbs, for example past-tense verbs, do not specify an action instance.

In various embodiments, and based at least in part on the type of actionpromised or requested in a communication, there may be additionalparameters of interest for the given action from the text. For instance,for a “send file” action, a desired parameter may include “File Name”and “File Type,” while for a calendar invite action, desired parametersmay include “List of Required Participants,” “List of OptionalParticipants,” “Time of the Event,” “Venue/Place,” “Subject of theEvent,” etc. in various embodiments and as would be understood by aperson having ordinary skill in the art upon reading the presentdisclosure.

Additional exemplary action instances in illustrative communications aredemonstrated in FIGS. 3A-3C, according to more embodiments. As definedabove, an action instance is a portion of a statement, focused around averb and one or more contextual elements of the statement, preferably atleast one contextual element positioned before (e.g. to the left, inEnglish) and at least one contextual element positioned after (e.g. tothe right, in English) of the focus verb. In each of the statements 300,310 and 320, the focus verb is represented by verb 302, while thecontextual element positioned before the focus verb 302 is representedby phrase 304 and the contextual element positioned after the focus verb302 is represented by phrase 306. Of course, in more complex statementsor communications, there may be multiple sentences, each potentiallyhaving multiple focus verbs and/or contextual elements beyond thesimplified scheme shown in FIGS. 3A-3C.

FIGS. 3A-3C show two actionable statements 300, 310 and a sentence 320that does not contain any actionable statement. In FIGS. 3A-3B, thesentences 300, 310 contain a promise and a request, respectively, andeach therefore contains one action instance. However, since thestatement 320 depicted in FIG. 3C lacks an action verb, it does notinclude an action instance.

Notably, and as demonstrated in FIGS. 3A-3C, the same tokens and tagscan appear in two action instances that belong to different classes(e.g., one promise and the other a request), for which their actionpatterns are different from each other. Hence, identifying/learningtokens and tags as individual elements does not help to correctlyidentify action instances and respective action classes.

In general, a sentence can have more than one action pattern when it hasmore than one verb (i.e., more than one action instance). For instance,consider the sentence “Please review the document and I will set up ameeting next week.” This exemplary sentence has two verbs (“review” and“set up”) that contribute to two different action instances. The first(“please review”) is a request and the second (“I will set up”) is apromise.

It is important, in such instances, to be able to recognize these twoaction instances separately rather than identifying the whole sentenceas an actionable statement. This is one reason that the presentlydisclosed inventive concepts, according to preferred approaches, areconfigured to identify the action patterns (e.g. Definition 3) thatcharacterize action instances.

Accordingly, in various embodiments actionable statement identification(e.g. according to Definition 4) may involve classifying action patternsinto correct classes. Identified actionable statements may then bepresented in a user friendly and interactive interface to cognitivelyassist communicators.

In preferred approaches the presently disclosed actionable statementidentification concepts include a training stage and a prediction stage.As one component of the adaptive nature of these inventiveimplementations, the training and prediction stages may be linked in afeedback system to facilitate continuous learning even while predictionis being carried out.

At a high level, this two-stage system 500 may be embodied as depictedin FIG. 5, in some approaches. As shown, the system 500 includes aplurality of modules 502-528. Each module 502-528 may be implemented viaone or more virtual and/or physical components of a computer system,such as a processor, a memory, one or more computer readable storagemedia, etc. as would be understood by a person having ordinary skill inthe art upon reading the present descriptions.

In the embodiment depicted in FIG. 5, training may be carried out usingmodules 502-512, while prediction may involve modules 522-526.Preferably, the system is adaptive via a feedback mechanism between thetraining and prediction portions, facilitating continuous adaptation tochanges in communication tendencies. Module 528 may include an outputfunctionality configured to output a conclusion of whether acommunication includes an actionable statement or not, and the type ofactionable statement (preferably promise, request, or other) asdescribed in further detail below.

The training portion of the system 500 preferably includes a featureextraction module 502 configured to identify features such as POS andNLP tags and tokens, verbs, and dependencies.

Training portion also includes an action verb learning module 504configured to adaptively learn action verbs, e.g. based on a trainingset and in response to being provided verbs from the feature extractionmodule 502. For instance, according to one approach the action verblearning module may be configured to determine whether a verb isactionable (e.g. based on tags applied by feature extraction module502). In response to determining the verb is actionable, action verblearning module may determine whether the verb is enclosed (againoptionally based on tags applied by the feature extraction module 502).In response to determining the action verb is not actionable, processingmay cease, imparting computational efficiency on the presently disclosedinventive concepts.

In response to determining the action verb is enclosed, the action verblearning module may be configured to determine the verb is dependent.Conversely, action verb learning module 504 may be configured todetermine the verb is independent in response to determining the verb isnot enclosed, in preferred approaches.

The action verb learning module 502 is also preferably configured tostore learned and/or identified action verbs in an action verb storagemodule 508.

Training portion also preferably includes a pattern construction module506 configured to construct patterns, e.g. of verbs, NLP and/or POS tagsand tokens, etc. and provide patterns to an adaptive, continuous patternlearning module 510.

Adaptive, continuous pattern learning module 510, in turn, is configuredto continuously and adaptively learn patterns, especially actionablestatement patterns.

In addition, modeling module 512 is configured to receive stored actionverbs from action verb storage module 508 and learned patterns fromadaptive, continuous pattern learning module 510, and in response toreceiving this information, generate and/or update a model foridentifying actionable statements in communications, using anytechniques as described herein as well as equivalents thereof that wouldbe understood by a person having ordinary skill in the art upon readingthe present disclosures. Accordingly, the model may be provided to theprediction portion by modeling module 510, in various embodiments.

Prediction portion of the system 500 may also include a featureextraction module 522, which is preferably configured to identify andprovide all features including one or more of POS tags, NLP tags, verbsand dependency information to a filtering module 524 of the predictionportion.

Filtering module 524, in turn, is preferably configured to determine, inresponse to receiving features from the feature extraction module 522,whether the statement includes an action verb. In response todetermining no action verb is included in the statement, filteringmodule 524 is preferably configured to output a negative determinationto output module 528 without permitting further computation to beperformed. Accordingly, filtering module 524 contributes tocomputational efficiency of the presently disclosed inventive conceptsin a manner that improves functioning of computers configured to performactionable statement identification.

In response to determining the statement includes an action verb,filtering module 524 is configured, in some approaches, to provide thestatement to pattern-based prediction module 526 for identification ofthe type of actionable statement included in the statement.

Accordingly, prediction portion of the system 500 may also include apattern-based prediction module 526 configured to analyze the statementpattern and predict the type of actionable statement included in thestatement. Statement type identification may be performed substantiallyas described herein, according to various embodiments. Upon predictingthe type of actionable statement, pattern-based prediction module 526 ispreferably configured to output the predicted actionable statement typeto output module 528, which may pass the prediction to a user orworkflow, e.g. for user feedback, cognitive assistance, etc. accordingto additional approaches and as described in further detail below.

Training

The learning phase, in various embodiments of the proposed approach,includes two components: (i) learning action verbs and (ii) learningaction patterns. As mentioned earlier, learning action verbs isimportant as this knowledge may be utilized as a filter during theprediction phase before pattern matching and recognition, thus improvingthe computational efficiency of the prediction process by omittinginappropriate statements from the prediction analysis. The patternlearning phase, in one embodiment, involves two sub-steps of patternconstruction (based on extracted featured), and adaptive patternlearning.

Identifying a verb as an action verb or not is important for theidentification of actionable statements. Recall that not all verbs areconsidered actionable, especially in the context of work communications,although they may appear in patterns that are identical to those ofaction patterns in some embodiments.

For example, consider the following sentence fragments. Example 1: “Iwill rest”, example 2: “I will meet”. Both sentence fragments havesimilar tokens except the verbs but the first one is not considered anaction instance because the action described by the verb is notconsidered important for the work environment.

It should be noted that having an action verb in a sentence is also notsufficient, alone, to make a statement an actionable statement, asdemonstrated by the example in FIG. 4A. In other words, having an actionverb in the statement is a necessary condition, but not sufficient tomake it an actionable statement, in preferred embodiments. Therefore,action verbs are preferably learned from training samples. Algorithms 1and 1.1, below show several proposed methods for learning differentforms and language constructs that action verbs appear in, according tomultiple embodiments.

Algorithm 1 Learning Action Verbs  1: input: String label, String verb,Set enclosed_verbs  2: output: updated verb Map  3: initialize or getexisting Map<Verb, Boolean>  4: if label ∈ Type then  5: if|enclosed_verbs| > 0 then  6: if Map does not contain verb then  7: Map← <verb, false>  8: end if  9: else 10: Map ← <verb, true> 11: end if12: end if

Algorithm 1.1: Learn Action Verbs Input:Class label, Verb, EnclosedVerbs, Verb Map if(tag is actionable statement type) if(Verb ∈ EnclosedVerbs) if(Verb ∉ Verb Map) Verb Map <− (Verb, false) end if else VerbMap <− (verb, true) end if end if

In particular, an action verb can be singular and independent, e.g.,such as “send” in the sentence “Please send me the file.”, or they canbe compound (appear with another action verb as enclosed), e.g., theverb “like” (which is not a singular, independent action verb), whichhas the enclosed verb “send” in the sentence “I would like you to sendme the file.” Advantageously, Algorithm 1 is able to learn both singularand compound action verbs.

In addition, an action verb can be singular and independent, e.g., suchas “send” in the sentence “Please send me the file.”, or they can becompound (appear with another action verb as enclosed), e.g., the verb“like” (which is not a singular, independent action verb), which has theenclosed verb “send” in the sentence “I would like you to send me thefile.” Advantageously, Algorithm 1.1 is able to learn both singular andcompound action verbs.

Compound action verbs can be complex in statements and be found throughfollowing a chain of enclosed verbs until a singular action verb isreached. In cases involving compound verbs such as “like”, it isadvantageous to record that the verb “like” can appear in actioninstances in the enclosed form.

In the above embodiment, according to Algorithm 1, if the label of theverb is of the class “promise” or “request,” and it is not enclosed withany other verb, then it is advantageous to record the verb with thevalue “true” (line 10). In other words, the verb appeared in a positivesample for one of the two classes of interest, and does not depend onanother verb.

If the labeled verb (to a class) encloses other verbs and there is norecord of the verb as an independent, singular form, it is advantageousto record the verb with the value “false” (line 7). This means that theverb cannot be used independently (alone) in action instances. Note thatit is advantageous to update an action verb as dependent if the verb wasnot ever discovered in an independent example (line 6).

According to the preferred two-step learning approach, the second stepin learning is the adaptive and continuous learning of action patternsfrom labeled samples. In one embodiment, these two steps arecontinuously performed in a production environment, meaning that thesystem continually performs training step as it encounters statementsthat it has not seen before (e.g. through user feedback) and/or inresponse to determining the user indicates a different class for astatement than determined using an automated technique as disclosedherein. The continuous learning may include considering adaptivelearning and personalization conditions and thresholds.

To learn action patterns from labeled samples, in one approach, oncefeatures are extracted for a given action verb in a labeled sample, itis advantageous to obtain the order of pronoun (PRON) token, auxiliary(AUX) token, and personal name (NER) tags, optionally including theorder of other token and other features (e.g. mood, action verbdesignation, whether or not other verbs are enclosed, etc.). In moreapproaches, it is advantageous to track some features at the tokenlevel, while others at the POS tag level.

For example, it is not necessary to record actual names of the peoplementioned in all training sentences as in this case only the POS tag(Person) is important and hence it is advantageous to use the NER taginstead of the actual token. On the other hand, it is important to learnactual tokens of pronouns and auxiliary words. For example, “I will” isa promise while “They will” is not considered a promise (the speaker isnot the one who is committing).

Indeed, tracking such features at the POS tags level according to oneembodiment leads to incorrect identification of a statement that fitsthe pattern as an action instance. An action pattern includes a set offeatures such as a sequence of patterns and tags (e.g. for NLP tagsrelated to the focus verb, such as NER, ACT, ACT_E, etc.; mood of thefocus verb; whether the focus verb is an action verb and/or enclosesother verb(s), etc.), and other independent features (e.g., verb tense).In particular, and according to one embodiment, during learning theproposed method takes the combined order of features (1) to (4) setforth above as one feature (they may to be considered together).Further, it is advantageous process and leverage the order of tokens andtags as they appear in the sentences of the NLP tags related to thefocus verb (e.g., the order of tags and tokens of NLP tags related tothe focus verb in FIG. 4A is “NER,could,you”). It may also be anadvantage to extract independent features (e.g. verb tense).

According to one implementation, training may be accomplished based inpart on adapting a classifier such as a Winnow-based multi-classclassifier for the presently disclosed learning process. The SparseNetwork of Winnows (SNOW) representation of such a classifier is similarto a one layer neural network but differs in how the classifier updatesweights. In particular, the weight updating process is mistake drivenand has the capability to both promote and demote weights.

According to various embodiments of the presently disclosed inventiveconcepts, the classification network has target nodes, and each inputfeature has weights associated with each target node. In preferredapproaches, the network will have target nodes as promise, request, andother. A positive prediction means the algorithm predicts a target node(>threshold) and negative prediction otherwise.

In particularly preferred approaches, node promotion/demotion may occuraccording to the following exemplary expressions. Let t be the targetnode let w_(t, i) be the weight of an active feature A_(i); let Q_(t) bethe threshold for the target node let α_(t) be the promotion constant;and let β_(t), be the demotion constant, respectively. Both promotionand demotion parameters are positive constants, where α_(t)>1 and0<β_(t)<1. Let A_(t)={A₁, . . . , A_(n)} be the set of active featurespresent in the current sample communication, for a given example linkedto the target node t.

In preferred approaches, employing Algorithm 1 promotes weights of thetarget node t as shown below in Equation 1 when the algorithm generatesa negative prediction for t according to the expression Σ_(iεA) _(t)w_(t,i)<Q_(t), yet the provided label (in training examples) matches t.∀_(i) ,w _(t,i) =w _(t,i)·α_(t)  (1)

In other words, for all active features A_(i) and corresponding weightsw_(t,i) of a target node t the weights are multiplied by the promotionconstant α_(t) in response to determining that the sum of weightsw_(t,i) for the node t and set of active features A_(t) is less than thenode threshold Q_(t) (causing a negative prediction to be generated fornode t) but the pattern actually is a correct match for the type ofactionable statement represented by node t.

In more approaches, in response to determining a particular patternmatches a different set of tokens, promotion may be utilized toreinforce the pattern match to the different set of tokens.

Conversely, in exemplary approaches a node may be demoted in response todetermining the sum of the weights is greater than the node threshold,but the pattern actually does not match the type of actionable statementrepresented by the node. Demotion may be performed, in preferredapproaches, according to Equation 2.∀_(i) ,w _(t,i) =w _(t,i)·β_(t)  (2)

When algorithm predicts negatively (does not predict) for a particularclass label, the weights for the “active features” are promoted for thenegatively predicted class, in several approaches.

When algorithm predicts positively but does not match the class label,then weights for the “active features” are demoted for the positivelypredicted class, in more approaches.

Preferably, all the other weights remain unchanged including those offeatures that are not present in the current sample. Weights are updatedbased on both the threshold and status of prediction, and stability isachieved when it is trained with enough samples. However, the presentlydisclosed inventive embodiments, as noted above, are adaptive and may beemployed without substantial training, although accuracy of predictionsmay be low in early applications of insufficiently trained embodiments(e.g. until stability is achieved).

In more embodiments, learning patterns may proceed according to thefollowing algorithm.

Algorithm 1.2: Learn Patterns Input: extracted features, Class Label,Verb, Enclosed Verbs, promotion_factor, demotion_factorlearnActionVerbs(Class Label, Verb, Enclosed Verbs) Category Weights W =getActiveFeatureWeights(extracted features) for each C_(i) classcategory Compute Weight (WC_(i)) = Σ (W_(j)) end for Winning_Category C= Max({WC_(i)}) if(C != Class Label) for each weight W_(i) of C, W_(i) =W_(i) * demotion_factor end for else if (C_(x) != C && C_(x) == ClassLabel) for each weight W_(i) of C_(x), W_(i) = W_(i) * promotion factorend for end if

The aforementioned algorithms adaptively train the classifier torecognize actionable statements such as promises and/or requests using asingle, global model. In more embodiments, it may be advantageous toadditionally and/or alternatively train models for particularindividuals, or groups of individuals (e.g. individuals sharing aparticular context influencing communication style, content ofcommunications, etc. as would be understood by a person having ordinaryskill in the art upon reading the present descriptions. Optionally,various embodiments of the presently disclosed inventive concepts mayinclude the ability to toggle involvement of personalized and/or sharedmodels to contribute to actionable statement identification (or not) fora particular set of communications and/or communicators.

Accordingly, in preferred approaches the presently disclosed inventiveconcepts include training one or more personalized models to identifyactionable statements. More preferably, the personalized model learningprocess is managed through a user feedback mechanism.

In one approach, during initial training, the system is bootstrappedusing the global model, which was trained using the training data. Theuser feedback may contribute to adapting the learned model and/orbuilding personalized models when the feedback is assessed to be ofpersonal opinion nature as described below. The users' feedback arepreferably collected in two forms: (i) tagging an identified action asnot being a request or a promise, and (ii) tagging a statement (indeed averb within a statement) as being a promise or request that is notcaptured.

The user feedback is assessed with respect to the matching actionpattern in the model, if one exists, and leads to the promotion ordemotion operation on the features' weights. In order to supportpersonalization, it is advantageous to employ two additional parametersassociated to each pattern: (1) a support score s, which keeps track ofthe number of times that a pattern has received a positive support for acertain class of statement from one or more individuals, and (2) theidentity of users who have supported the pattern.

In more embodiments, it is advantageous to apply a threshold γ on s foreach pattern to identify which model level (e.g. global, shared, orpersonalized) the score belongs to. For a given action pattern AP, whens(AP)<γ, the pattern would stay active only for the users who supportit, in one approach. And, if s(AP)≧γ, it is preferably promoted to thehigher level model in the hierarchy (e.g. a global or shared model,depending on the configuration).

In several approaches, where user feedback is determined to indicatedemotion of a given pattern AP in the global model, but for which afterdemotion still s(AP)≧γ, s remains active in the global model, but itwould be recorded in the personal model of the user who demoted it as anexcluded pattern. The described personalized model allows the user tokeep personal patterns in their personalized model, even when thosepersonalized patterns are not supported by system-wide or domain-widesupport (for a shared model). Essentially, individuals may “opt-out” ofparticipation in the higher level models with respect to particularpatterns. At the same time, an individual's decision to demote aparticular recognition does not disrupt the global or shared recognitionreflected in the corresponding models unless sufficient consensus isreached to cross the threshold γ.

According to even more embodiments, training may involve training sharedmodels. The learning of a shared model can be bootstrapped through atraining phase with samples labeled with the specific domain name forthe model. Domain models can also be adapted through user feedback byusers who are tagged as belonging to that domain. Shared modelconfiguration in the system may also be associated to a domain supportscore threshold σ<<γ. When shared models are enabled for a specificuser, according to one embodiment action patterns with a support score sbelow γ are kept in personal models. In more embodiments, if σ<s(AP)<γ,the action pattern belongs to the shared model, and s(AP)≧γ the patternmay be stored in the global model.

Accordingly, the presently disclosed inventive concepts preferablyinclude evaluating and determining values of the foregoing supportscores, and taking appropriate promotion/demotion action in response todetermining the support score has a value greater, less than, or equalto the above thresholds according to the foregoing expressions.

Various embodiments of these concepts may include any one or more oftraining global, shared, and/or personalized models, in variousapproaches.

Prediction

In the prediction stage, according to one embodiment it is advantageousto select the highest aggregated weighted class as the predictedactionable statement type (i.e., employing a winner takes all paradigm).In rare but possible occasions of a tie, it is advantageous to make thedecision based on a separate support score based on learning shared orpersonalized models (described above) to select one of promise orrequest classes.

Prediction may preferably be performed substantially according to thefollowing descriptions, in various embodiments.

In one approach, prediction is a two-step process. In the first step, itis advantageous to determine whether the verb is designated as an actionverb (either as independent or dependent). In the second step, patternmatching-based identification is performed. This two-stageidentification process advantageously avoids keeping track of every verbin the action patterns which can explode the number of action patternsit has to learn.

According to one exemplary embodiment, Algorithm 2 below outlines stepsin an actionable statement identification process according to thepresently disclosed inventive concepts. In essence, the algorithmoperatively predicts the class of an actionable statement for theextracted features in response to determining the verb is an independentor dependent action verb (lines 7-8).

In more approaches, the prediction process preferably takes intoconsideration the enclosed verb set. If the verb is not recorded as anindependent or dependent action verb, it is advantageous to predict theclass as “other” (line 10).

Algorithm 2 Action Pattern Recognition  1: input: Sentence s  2: output:Map verb_type  3: List verbs ← extract Verbs(s)  4: for all v ∈ verbs do 5: List enclosed_verbs ← extractEnclosed(s,v)  6: List features ←ExtractFeatures(s,v)  7: if isLearnedActionVerb(v) then  8: verb_type ←predictType(v, features, en- closed_verbs)  9: else 10: verb_type ←<v,other_class> 11: end if 12: end for 13: return verb_type

Preferably, this model is able to learn the set of action verbs from thetraining samples (i.e., bootstrapped). In one implementation, a trainingset may be populated with some seed action verbs taken from a particulardomain and/or existing users of an enterprise email client, chat ormessaging system, social media service, etc. as would be understood by aperson having ordinary skill in the art upon reading the presentdescriptions.

In preferred approaches, during the process of actionable statementidentification, priority is given to patterns in the personalizedmodels, followed by shared models and global models. This facilitatesconsidering personalized models, and domain models over the patterns inthe global model. It should be noted that not every user may have apersonal model (in case they have not provided any feedback, or whentheir feedback are applied to patterns in global and domain levels), invarious approaches. Similarly, various embodiments may omit a domainspecific and/or shared model, and be implemented using a global modelexclusively. Further still, some embodiments may include utilizing oneor more arbitrarily defined model “levels”, e.g. for a department,organization, domain, geographical location, etc. as would be understoodby a person having ordinary skill in the art upon reading the presentdescriptions.

In more embodiments, actionable statement class prediction may beperformed substantially according to the following Algorithm 2.1.

Algorithm 2.1: Predict Actionable Statement Class Input: extractedfeatures, Verb, enclosed Verbs, Verb Map Output: Action class CategoryWeights W = getActiveFeatureWeights(extracted features)if(isIndependentActionVerb(Verb, Verb Map) for each C_(i) class categoryCompute Weight (WC_(i)) = Σ (W_(j)) end for Winning_Category C =Max({WC_(i)}) return C else if(isDependentActionVerb(Verb, Verb Map))if(IsIndependentActionVerb(enclosed Verbs, Verb Map) for each C_(i)class category Compute Weight (WC_(i)) = Σ (W_(j)) end forWinning_Category C = Max({WC_(i)}) return C else return “other categoryclass” end if else return “other category class” end if

Model Organization

Turning now to FIG. 6, an exemplary organization 600 for various modelsof actionable statement adaptive learning is shown, according to oneembodiment. The organization 600, in various approaches, may include oneor more layers. The number of layers may depend upon the context inwhich the organization 600 is employed, and preferably represents anetwork of one or more individuals and groups of individuals.

As shown in FIG. 6, and according to a preferred approach, theorganization 600 includes three layers.

A first layer includes one or more personalized models 604, each beingconfigured to adaptively and/or continuously learn an individual'spersonal communication and behavior patterns, and utilize the learningprocess to effectively and efficiently identify actionable statementswithin communications in which the individual is involved and/orreferenced. The personalized models 604 may be represented as apersonalized actionable insights graph 602

A second layer includes one or more shared models 606, each beingconfigured to adaptively and/or continuously learn a group ofindividuals' communication and behavior patterns, and utilize thelearning process to effectively and efficiently identify actionablestatements within communications in which one or more of the group ofindividuals is involved and/or referenced. In preferred approaches, theshared models 606 are configured to adaptively and/or continuously learnto identify actionable statements for a plurality of the individuals forwhich the personalized models 604 are configured to adaptively and/orcontinuously learn to identify actionable statements. As such, theshared models 606 may be based at least in part on one or more of thepersonalized models 604, in some approaches.

As will be understood by a person having ordinary skill in the art uponreading the present disclosure, not all of the personalized models 604need be utilized to define or configure the shared models 606, and notall individuals for which personalized models 604 are configured need beserved by a shared model 606.

Groups of individuals, in different embodiments, may include anysuitable partition of the population in the context of various types ofcommunications that occur in daily life. For example, groups may includeindividuals involved in various different economic sectors, such asindividuals involved in a particular career, profession, etc.,individuals employed by a particular employer, in a particulargeographic location (e.g. according to primary language spoken), etc. aswould be understood by a person having ordinary skill in the art uponreading the present descriptions.

In more embodiments, groups may include individuals that performparticular roles in the context of an activity or communication, such asindividuals responsible for providing products or services, individualsentitled to receive products or services, supervisors, subordinates,individuals working in a particular department and/or on a particularproject, etc. as would be understood by a person having ordinary skillin the art upon reading the present descriptions.

Again as shown in FIG. 6, the organization 600 includes a third layercomprising a global actionable statement model 608. The globalactionable statement model 608 is configured to adaptively and/orcontinuously learn one or more individuals' and/or groups ofindividuals' communication and behavior patterns, and utilize thelearning process to effectively and efficiently identify actionablestatements within communications in which one or more of the individualsand/or groups of individuals is involved and/or referenced.

So as to facilitate optimal learning and continuous adaptation tochanging communication styles, new terminology, new or differentbehaviors or behavior patterns, etc., the global actionable statementmodel 608 is preferably configured to adaptively and/or continuouslylearn to identify actionable statements in communications for allindividuals for which a personal model 604 is configured and/or groupsof individuals for which a shared model 606 is configured. Morepreferably, the global actionable statement model 608 is preferablyconfigured to adaptively and/or continuously learn to identifyactionable statements in communications for each individual and group ofindividuals in the network.

In addition, the global actionable statement model 608 may be configuredto learn to identify actionable statements based at least in part on oneor more of the personalized models 604 and/or shared models 606.

In addition, and in preferred approaches, the global actionablestatement model 608 preferably includes a plurality of models. Each ofthe plurality of models may be configured to adaptively and/orcontinuously learn in a particular context of communication. Forinstance, in particularly preferred embodiments the plurality of modelsinclude a statement model 610 configured to adaptively and/orcontinuously learn in the particular context of statements; an actionmodel 612 configured to adaptively and/or continuously learn in theparticular context of actions; and a prioritization model 614 configuredto adaptively and/or continuously learn in the particular context ofprioritization.

In some approaches, statement model 610 may be configured to adaptivelyidentify actionable statements in communications exchanged betweenentities within a communication network.

In various embodiments, action model 612 may be configured to determinean appropriate action to be taken in response to an actionablestatement. Exemplary actions may include organizational tasks such ascreating a reminder, adding an item to a to-do list, setting a follow-upperiod for responsive action, etc. as would be understood by a personhaving ordinary skill in the art upon reading the present descriptions.Exemplary actions may also include scheduling tasks such as scheduling ameeting, scheduling a processing job, setting a deadline, etc. as wouldbe understood by a person having ordinary skill in the art upon readingthe present descriptions. Exemplary actions may further includecommunication tasks such as sending an email, message, posting to asocial medium, etc. as would be understood by a person having ordinaryskill in the art upon reading the present descriptions. Exemplaryactions may still further include delivery tasks such as deliveringrequested information to a requesting entity, e.g. delivering data, areport, a written communication, etc. as would be understood by a personhaving ordinary skill in the art upon reading the present descriptions.

Similarly, in additional embodiments the prioritization model 614 may beconfigured to determine an appropriate priority for an actionablestatement or statements, e.g. a priority to assign to a message for theuser's review, a priority for an appropriate action to be taken inresponse to receiving the communication including the actionablestatement, etc. as would be understood by a person having ordinary skillin the art upon reading the present descriptions. In preferredapproaches, prioritization model 614 is configured to determine apredetermined priority level selected from urgent, important, and low.

The plurality of models forming the global actionable statementidentification model 608 may also cooperatively operate to facilitatecontextually appropriate identification of actionable statements incommunications, and provide cognitive assistance to individuals involvedin those communications, in preferred embodiments.

In more approaches, the various models in each layer of the organization600 are preferably organized in a hierarchical fashion, substantially asdepicted in FIG. 6.

Of course, in some embodiments it is advantageous to includecapabilities for promoting or demoting particular models from one layerof the organization 600 to another.

In addition, it is advantageous to allow individuals to designatewhether or not a particular model, particularly a shared model 606and/or global actionable statement identification model 608, should beemployed to provide cognitive assistance with respect to theircommunications. For instance, within an enterprise having variousdifferent departments responsible for different tasks (e.g. legal,financial, production, sales, research and development, etc.) may have aplurality of shared models 606, each being configured to identifyactionable statements in communications for one of the departments.Individuals working in one department may wish to disable shared models606 configured to identify actionable statements for another department,as those models may be inapplicable to the terminology and/or context ofcommunications in the individual's department.

In implementation of the presently disclosed inventive embodiments, itwas found that these adaptive actionable statement identificationtechniques provide superior performance to existing techniques foridentifying actionable statements. For instance, one test case comparingthe presently disclosed inventive concepts to a manual rule writingprocess resulted in the adaptive actionable statement identificationtechniques identifying 55 of 57 actionable statements in a test dataset, where the manual rule writing process only identifies 49 of the 57.This superior performance also contributes to the scalability of theinventive concepts, as the ability to adaptively identify actionablestatements using a robust automated process as described herein coupledwith high accuracy and precision provides flexibility to the system andallows tolerance of adding new communicators, new styles ofcommunication, etc. as would be understood by a person having ordinaryskill in the art upon reading the present descriptions.

Applications

During the actionable statement learning and identification, the processmay not extract all desired information about the actionable statementswhich are important for a comprehensive presentation of the action tothe user, specifically those that are mentioned beyond the sentence inwhich actionable statement is expressed in. Depending on the actionclass (e.g. promise, request, question, instruction, withdrawal, etc. asset forth herein) and also the type of the action (e.g. respond, sendfile, calendar invite, etc.), it is desirable to extract specificinformation attributes (metadata). In particular, we define andrecognize a set of action types such as calendar-related, send document,reminder, etc., using a set of rules (optionally written in a querylanguage such as AQL).

Each of these types of action have distinct set of desired attributes inaddition to those of action classes of promise or request. For eachaction type, it is advantageous to define a template that includes of aset of desired features to be extracted and gathered from the text ofconversation among participants who exchange information about thataction.

For example, in various exemplary embodiments the template for acalendar action type contains the following attributes: invitechair/organizer, required participants, optional participants, subject,meeting time, and meeting location.

FIG. 3B shows an example for action type “send document” where it isrequesting to share a document. For this type, the desired features inone approach are document name, document type, or a set of keywords thatare input to a search function that looks for the matching documentcandidates.

This extraction process may preferably be guided through pre-definedtemplates for each action type. For instance, the extraction process mayemploy three techniques in order to find the template attributes beyondthe sentence that contain the action verb: (i) co-reference resolutionmay be used to link different sentences depending on the common entitiesthat are referenced among them, (ii) looking for missing values ofattributes in the template in the neighboring sentences, before (forPerson Names and Named Entities, often) and after (e.g. for furtherdetails about timing, location, etc. in case of calendar action types),and for any implicit entity links between sentences; and (iii) lookingthrough the conversation threads on the same topic (e.g., email chains)if the same action is discussed in prior conversations. Using thesetechniques allows the method to populate the template with informationfor template attributes that are then present to the user.

Accordingly, the presently disclosed inventive actionable statementidentification learning and prediction processes convey advantages tothe field of electronic communications by providing cognitive assistanceto users, enabling the users to efficiently and effectively address thelarge volume of communications with which working individuals are facedon a daily basis, and which otherwise are prone to delay and/or failureto respond, and corresponding detrimental real-world consequences.

Now referring to FIG. 7, a flowchart of a method 700 is shown accordingto one embodiment. The method 700 may be performed in accordance withthe present invention in any of the environments depicted in FIGS. 1-6,among others, in various embodiments. Of course, more or less operationsthan those specifically described in FIG. 7 may be included in method700, as would be understood by one of skill in the art upon reading thepresent descriptions.

Each of the steps of the method 700 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 700 may be partially or entirely performed by aprocessor, or some other device having one or more processors therein.The processor, e.g., processing circuit(s), chip(s), and/or module(s)may be implemented in hardware and/or software, and preferably having atleast one hardware component may be utilized in any device to performone or more steps of the method 700. Illustrative processors include,but are not limited to, a central processing unit (CPU), a graphicsprocessing unit (GPU), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA), etc., combinationsthereof, or any other suitable computing device known in the art.

As shown in FIG. 7, method 700 may initiate with operation 702, wherefeatures are extracted from at least one training statement.

Method 700 may also include operation 704, in which an action verbmodule is optionally trained to identify dependency (e.g. dependentversus independent) of actionable verb statements. The identification ispreferably based on some or all of the extracted features, and morepreferably based on verbs.

Method 700 also includes operation 706, where an actionable verbdictionary is optionally built using the trained action verb module.

In operation 708, a pattern recognition module is trained to identifyone or more types of patterns in actionable statements based at least inpart on the features. Preferably, all extracted features are used totrain the pattern recognition module to provide a robust representationof actionable statement types and facilitate stability in prediction ofactionable statement types during subsequent prediction.

In operation 710, an actionable statement identification model isgenerated based on the trained action verb module and the trainedpattern recognition module.

Of course, in various approaches method 700 may include one or moreadditional and/or alternative features and/or operations. For instance,according to one embodiment the method 700 may include performingadaptive training on the actionable statement identification model basedon one or more sample statements. More preferably, adaptive training isperformed continuously as a prediction module predicts types ofactionable statements present in one or more sample statements.

Preferably, the adaptive training is mistake-driven. Mistake-drivenadaptive training, according to various approaches, may includepromoting one or more weights corresponding to a particular actionablestatement type at least partially in response to predicting anactionable statement type is different from the predicted actionablestatement type; and/or demoting one or more weights corresponding to aparticular actionable statement type at least partially in response todetermining a predicted actionable statement type is the predictedactionable statement type.

The promoting is preferably performed in further response to determiningthe actionable statement type is the particular actionable statementtype, and the demoting is preferably performed in further response todetermining the actionable statement type is not the particularactionable statement type

In several approaches, the extracted features include one or more of:verbs; part of speech (POS) tags, POS tokens; natural language parsing(NLP) tags; NLP tokens; context phrases; and dependency information.Moreover, the context phrases may include at least one phrase occurringbefore an action verb in one or more of the training statements and atleast one phrase occurring after the action verb in one or more of thetraining statements.

In additional embodiments, the action verb module is trained to identifydependency of actionable verb statements based on determining whether anaction verb is enclosed by another verb.

Now referring to FIG. 8, a flowchart of a method 800 is shown accordingto one embodiment. The method 800 may be performed in accordance withthe present invention in any of the environments depicted in FIGS. 1-6,among others, in various embodiments. Of course, more or less operationsthan those specifically described in FIG. 8 may be included in method800, as would be understood by one of skill in the art upon reading thepresent descriptions.

Each of the steps of the method 800 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 800 may be partially or entirely performed by aprocessor, or some other device having one or more processors therein.The processor, e.g., processing circuit(s), chip(s), and/or module(s)may be implemented in hardware and/or software, and preferably having atleast one hardware component may be utilized in any device to performone or more steps of the method 800. Illustrative processors include,but are not limited to, a central processing unit (CPU), a graphicsprocessing unit (GPU), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA), etc., combinationsthereof, or any other suitable computing device known in the art.

In various embodiments, the method 800 is preferably performed inconjunction with performing a training technique, such as shown in FIG.7, according to one embodiment. More preferably, training and predictionare performed continuously to facilitate adaptive capabilities of theactionable statement identification process.

As shown in FIG. 8, method 800 may initiate with operation 802, astatement is analyzed to determine whether the statement includes anactionable statement.

In operation 804, and in response to determining the statement includesan actionable statement, an actionable statement class for theactionable statement is predicted, based on a pattern represented in thestatement. The actionable statement class is selected from among aplurality of actionable statement classes comprising promise andrequest.

Method 800 also includes operation 806, in which the predictedactionable statement class is output to a user, e.g. via a display.

Of course, in various approaches method 800 may include one or moreadditional and/or alternative features and/or operations. For instance,according to one embodiment the method 800 may include performingadaptive training on the actionable statement identification model basedon one or more sample statements. More preferably, adaptive training isperformed continuously as a prediction module predicts types ofactionable statements present in one or more sample statements.

For instance, in one embodiment the predicting is performed using anactionable statement identification model, and method 800 includesadaptively training the actionable statement identification model basedon the predicting.

The method 800 may additionally and/or alternatively include determiningwhether the statement includes an actionable statement comprisesdetermining whether the statement includes one or more action verbs.

In more embodiments, the method 800 includes, in response to determiningthe statement does not include an actionable statement, outputting anindication that the statement does not include an actionable statementwithout performing the predicting.

Additionally and/or alternatively, the predicting is performed atverb-level granularity. In other words, according to preferredembodiments a single statement may include multiple verbs, and each verbmay be individually analyzed (preferably in context of surroundingwords, which may include other verbs separately analyzed) so thatstatements containing multiple verbs may be effectively and efficientlyanalyzed to determine whether a single, or multiple action verbs and/orverb types are present in the statement.

The method may also include predicting an actionable statement class ofa second actionable statement included in the statement, in someapproaches.

The predicting may include determining an actionable statement classhaving an associated weight with a value higher than one or more weightsassociated with other of the actionable statement classes, in moreapproaches.

In response to determining no single actionable statement class has anassociated weight with a value higher than one or more weightsassociated with other of the actionable statement classes, method 800may include evaluating a support score of one or more of the actionablestatement classes.

In various embodiments, the predicting is based on a personalizedactionable statement model, a shared actionable statement model; or aglobal actionable statement model.

The pattern upon which prediction is based may include an ordering ofone or more features extracted from the statement; and the one or morefeatures are preferably selected from verbs; part of speech (POS) tags,POS tokens; natural language parsing (NLP) tags; NLP tokens; contextphrases; and dependency information.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Moreover, a system according to various embodiments may include aprocessor and logic integrated with and/or executable by the processor,the logic being configured to perform one or more of the process stepsrecited herein. By integrated with, what is meant is that the processorhas logic embedded therewith as hardware logic, such as an applicationspecific integrated circuit (ASIC), a FPGA, etc. By executable by theprocessor, what is meant is that the logic is hardware logic; softwarelogic such as firmware, part of an operating system, part of anapplication program; etc., or some combination of hardware and softwarelogic that is accessible by the processor and configured to cause theprocessor to perform some functionality upon execution by the processor.Software logic may be stored on local and/or remote memory of any memorytype, as known in the art. Any processor known in the art may be used,such as a software processor module and/or a hardware processor such asan ASIC, a FPGA, a central processing unit (CPU), an integrated circuit(IC), a graphics processing unit (GPU), etc.

It will be clear that the various features of the foregoing systemsand/or methodologies may be combined in any way, creating a plurality ofcombinations from the descriptions presented above.

It will be further appreciated that embodiments of the present inventionmay be provided in the form of a service deployed on behalf of acustomer to offer service on demand.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A computer-implemented method for automaticallyidentifying actionable statements in electronic communications, themethod comprising: obtaining a first set of rules that define actionablestatements as a function of tags, tokens, and contextual elementsassociated with target verbs; extracting features from at least onetraining statement based on the first set of rules, wherein the featuresinclude one or more of tags and language token types for words of thestatement; training a pattern recognition module to identify one or moretypes of patterns in actionable statements based at least in part on thefeatures; and training an action verb module to identify dependency ofthe actionable statements based on at least some of the features;generating an actionable statement identification model using thetrained action verb module and the trained pattern recognition module,the actionable statement identification model including a plurality oftarget nodes, each node having associated therewith one or more activefeatures and one or more corresponding weights of the one or more activefeatures; adaptively training the actionable statement model by at leastone of promoting and demoting the weights corresponding to the activefeatures associated with some or all of the plurality of target nodes,wherein weights corresponding to the active features are promoted inresponse to determining a true positive match between active features ofa particular statement within an electronic communication and the activefeatures to which the weights correspond; and wherein weightscorresponding to the active features are demoted in response todetermining a false positive match between active features of aparticular statement within an electronic communication and the activefeatures to which the weights correspond; receiving an incomingelectronic communication; extracting the features of the words andphrases in statements of the incoming electronic communication;filtering the statements to identify the statements that include one ormore action verbs as actionable statements and the statements that donot include any action verbs as filtered out statements; outputting thestatements that were removed by the filtering to a user for userfeedback; analyzing statement patterns of the actionable statements topredict a type of each of the actionable statements, by applying theactionable statement identification model to the actionable statements;outputting a predicted actionable statement type to the user for userfeedback; and performing continual training of the actionable statementidentification model by providing the user feedback identifyingstatements that haven not been seen before or the user feedbackindicating a different type for a statement than the predicted type tothe actionable statement identification model.
 2. The method as recitedin claim 1, comprising: building an actionable verb dictionary using thetrained action verb module; and wherein the adaptive training isperformed continuously as a prediction module predicts types ofactionable statements present in one or more sample statements.
 3. Themethod as recited in claim 1, wherein the action verb module is trainedto identify dependency of actionable verb statements based ondetermining whether an action verb is enclosed by another verb.
 4. Thecomputer-implemented method as recited in claim 1, wherein weightscorresponding to the active features are promoted in response todetermining a true positive mismatch between active features of aparticular statement within an electronic communication and the activefeatures to which the weights correspond.
 5. The computer-implementedmethod as recited in claim 1, wherein promoting the weightscorresponding to the active features associated with some or all of theplurality of target nodes comprises, for all active features A_(i) andcorresponding weights w_(t,i) of a target node t among the plurality oftarget nodes, multiplying the corresponding weights w_(t,i) by apromotion constant α_(t) in response to determining that a sum of theweights w_(t,i) for the node t and set of active features A_(t) is lessthan a node threshold Q_(t).
 6. The computer-implemented method asrecited in claim 1, wherein demoting the weights corresponding to theactive features associated with some or all of the plurality of targetnodes comprises, for all active features A_(i) and corresponding weightsw_(t,i) of a target node t among the plurality of target nodes,multiplying the corresponding weights w_(t,i) by a demotion constantβ_(t) in response to determining that a sum of the weights w_(t,i) forthe node t and set of active features A_(t) is greater than a nodethreshold Q_(t).
 7. The computer-implemented method as recited in claim1, wherein the plurality of target nodes comprise a “promise” node, a“request” node, and an “other” node.
 8. The computer-implemented methodas recited in claim 1, wherein the tags and token types correspond toone or more of: a pronoun (PRON) token; an auxiliary (AUX) token; one ormore personal named entities (NER) tags relating to a focus verb presentin the training statement; a mood of one or more verbs present in thetraining statement; whether one or more verbs present in the trainingstatement are action verbs; whether one or more verbs present in thetraining statement enclose another verb or verbs present in the trainingstatement; whether a number of enclosed action verbs present in thetraining statement is greater than zero; and a tense of at least oneverb present in the training statement.
 9. The computer-implementedmethod as recited in claim 8, wherein the mood of the one or more verbspresent in the training statement are selected from: normal, imperative,and infinitive.
 10. The computer-implemented method as recited in claim8, wherein past-tense verbs present in the training statement aredisqualified from being considered action verbs.
 11. Thecomputer-implemented method as recited in claim 1, wherein the featuresare extracted using an Annotation Query Language (AQL).
 12. Thecomputer-implemented method as recited in claim 1, wherein the actionverb module is trained to identify dependency of actionable verbstatements based on determining whether an action verb is enclosed byanother verb; wherein weights corresponding to the active features arepromoted in response to determining a true positive mismatch betweenactive features of a particular statement within an electroniccommunication and the active features to which the weights correspond;wherein promoting the weights corresponding to the active featuresassociated with some or all of the plurality of target nodes comprises,for all active features A_(i) and corresponding weights w_(t, i) of atarget node t among the plurality of target nodes, multiplying thecorresponding weights w_(t,i) by a promotion constant α_(t) in responseto determining that a sum of the weights w_(t,i) for the node t and setof active features A_(t) is less than a node threshold Q_(t); andwherein the plurality of target nodes comprise a “promise” node, a“request” node, and an “other” node.
 13. A computer program product foridentifying actionable statements in electronic communications, thecomputer program product comprising a computer readable storage mediumhaving program instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to: obtain a first setof rules that define actionable statements as a function of tags,tokens, and contextual elements associated with target verbs; extractfeatures from at least one training statement based on the first set ofrules, wherein the features include one or more of tags and languagetoken types for words of the statement; train a pattern recognitionmodule to identify one or more types of patterns in actionablestatements based at least in part on the features; and train an actionverb module to identify dependency of the actionable statements based onat least some of the features; generate an actionable statementidentification model using the trained action verb module and thetrained pattern recognition module, the actionable statementidentification model including a plurality of target nodes, each nodehaving associated therewith one or more active features and one or morecorresponding weights of the one or more active features; adaptivelytrain the actionable statement model by at least one of promoting anddemoting the weights corresponding to the active features associatedwith some or all of the plurality of target nodes, wherein weightscorresponding to the active features are promoted in response todetermining a true positive match between active features of aparticular statement within an electronic communication and the activefeatures to which the weights correspond; and wherein weightscorresponding to the active features are demoted in response todetermining a false positive match between active features of aparticular statement within an electronic communication and the activefeatures to which the weights correspond; receive an incoming electroniccommunication; extract the features of the words and phrases instatements of the incoming electronic communication; filter thestatements to identify the statements that include one or more actionverbs as actionable statements and the statements that do not includeany action verbs as filtered out statements; output the statements thatwere removed by the filtering to a user for user feedback; analyzestatement patterns of the actionable statements to predict a type ofeach of the actionable statements, by applying the actionable statementidentification model to the actionable statements; output a predictedactionable statement type to the user for user feedback; and performcontinual training of the actionable statement identification model byproviding the user feedback identifying statements that haven not beenseen before or the user feedback indicating a different type for astatement than the predicted type to the actionable statementidentification model.
 14. A system, comprising: a processor and logicintegrated with and/or executable by the processor, the logic beingconfigured to cause the processor to: determine whether a statement,within an electronic communication, includes an actionable statement,wherein determining whether a statement includes an actionable statementcomprises: obtaining a first set of rules that define actionablestatements as a function of tags, tokens, and contextual elementsassociated with target verbs; extracting features from at least onetraining statement based on the first set of rules; training a patternrecognition module to identify one or more types of patterns inactionable statements based at least in part on the features; andtraining an action verb module to identify dependency of the actionablestatements based on at least some of the features; generating anactionable statement identification model using the trained action verbmodule and the trained pattern recognition module, the actionablestatement identification model including a plurality of target nodes,each node having associated therewith one or more active features andone or more corresponding weights of the one or more active features;adaptively training the actionable statement model by at least one ofpromoting and demoting the weights corresponding to the active featuresassociated with some or all of the plurality of target nodes, whereinweights corresponding to the active features are promoted in response todetermining a true positive match between active features of aparticular statement within an electronic communication and the activefeatures to which the weights correspond; and wherein weightscorresponding to the active features are demoted in response todetermining a false positive match between active features of aparticular statement within an electronic communication and the activefeatures to which the weights correspond; in response to determining thestatement includes an actionable statement, predicting, from among aplurality of potential actionable statement classes, an actionablestatement class of the actionable statement, wherein the prediction isbased on a pattern represented in the statement and comprises: computinga weight of each of the plurality of potential actionable statementclasses based on the pattern represented in the statement; anddetermining from among the plurality of potential actionable statementclasses, an actionable statement class having an associated weight witha value higher than weights associated with other of the plurality ofpotential actionable statement classes is the predicted actionablestatement class of the actionable statement; and outputting thepredicted actionable statement class of the actionable statement to auser; receiving feedback from the user in response to outputting thepredicted actionable statement class; providing the user feedback to theactionable statement model to facilitate adaptively training theactionable statement model; and wherein the predicted actionablestatement class is selected from among a plurality of actionablestatement classes comprising promises.
 15. The system as recited inclaim 14, comprising, in response to determining the statement does notinclude an actionable verb, outputting an indication that the statementis not an actionable statement without performing the predicting; inresponse to determining no single actionable statement class has anassociated weight with a value higher than one or more weightsassociated with other of the actionable statement classes, evaluating asupport score of one or more of the actionable statement classes; andwherein the predicting comprises determining an actionable statementclass having an associated weight with a value higher than one or moreweights associated with other of the actionable statement classes;wherein the predicting is based on at least a personalized actionablestatement model and a shared actionable statement model; and wherein thepersonalized actionable statement model is given priority over theshared actionable statement model; wherein the action verb module istrained to identify dependency of actionable verb statements based ondetermining whether an action verb is enclosed by another verb; whereinweights corresponding to the active features are promoted in response todetermining a true positive mismatch between active features of aparticular statement within an electronic communication and the activefeatures to which the weights correspond; wherein promoting the weightscorresponding to the active features associated with some or all of theplurality of target nodes comprises, for all active features At andcorresponding weights w_(t),i of a target node t among the plurality oftarget nodes, multiplying the corresponding weights w_(t),i by apromotion constant at in response to determining that a sum of theweights w_(t),i for the node t and set of active features A_(t) is lessthan a node threshold Q_(t); and wherein the plurality of target nodescomprise a “promise” node, a “request” node, and an “other” node.